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Record W2959895731 · doi:10.6002/ect.2017.0096

Immunosuppression Practices in Liver Transplantation: A Survey of North American Centers.

2017· article· en· W2959895731 on OpenAlexaffabout
Trana Hussaini, Ricky D. Turgeon, Nilufar Partovi, Siegfried R. Erb, Charles H. Scudamore, Eric M. Yoshida

Bibliographic record

VenueExperimental and Clinical Transplantation · 2017
Typearticle
Languageen
Field
Topic
Canadian institutionsVancouver General Hospital
Fundersnot available
KeywordsImmunosuppressionMedicineLiver transplantationClinical PracticeIntensive care medicineInternal medicineFamily medicineTransplantation

Abstract

fetched live from OpenAlex

OBJECTIVES: There is a clear lack of clinical evidence guiding immunosuppressive management in long-term stable liver transplant recipients. As a result, anecdotal experience suggests wide variability across transplant centers. We aimed to identify patterns of immunosuppression practices in liver transplant centers across Canada and the United States. MATERIALS AND METHODS: From February 9 to May 31, 2015, we invited clinicians from all liver transplant centers in Canada and the United States to answer a 6-question survey generated using SurveyMonkey. RESULTS: Seventeen respondents from 15 liver transplant centers completed the survey. Although immun-suppressive practices are relatively uniform for induction and early maintenance therapy, significant variations exist in the management of long-term immunosuppression in stable transplant recipients with a relative lack of minimization protocols. CONCLUSIONS: Our survey confirms a wide variability in immunosuppression practices across Canadian and US liver transplant centers. Research and practice priorities include design of pragmatic randomized controlled trials and development of clinical practice guidelines to standardize immunosuppressive management of long-term stable liver transplant recipients with a focus on immunosuppression minimization.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.103
GPT teacher head0.427
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2017
Admission routes2
Has abstractyes

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